#开源/仓库

Today, we're releasing the Carbon Annotation Database and the model behind it, Carbon-A. We've used this model to discover 566.34 million new gene candidates across 22,617 species, thousands of which have never been studied like this before. We wet-lab validated a number of these new genes in well-studied species, such as in cats, chicken, and arabidopsis (plant), with our partners at Active Site and @UCSD, with more extensive wet lab results on previously-unstudied genomes coming soon.

an open model just beat Jev outright on clinical decisions 🔥 pplx-decider v1.1 got 643 of 669 right vs 628 for Jev, at 42% less per decision and about the same speed. and you can download the weights and run it inside the hospital. System One just went open

We open sourced Octop, a multi-user AI workspace for yourself, your family and your team. Everyone gets their own login and can create multiple agents, each with its own workspace. Nothing you say or save shows up in another account. Almost everything can be swapped: models, storage, memory, plugins. Each agent comes with a browser, a terminal and a remote desktop. You can hand off work to Claude Code, Codex, Cursor, OpenCode, CodeBuddy and other coding agents you already use. It also lives in your group chats on Telegram and Discord. It runs on your own machine, and one command is all it takes: octop run. The full picture is in the repo: http://github.com/TencentCloud/Octop.

我们发布 pplx-embed-v2-late，两个后期交互嵌入模型，通过共享嵌入空间检索文本、图像和页面，实现跨模态查询。 两个模型均达到前沿性能，并已在 Hugging Face 上公开可用。 https://www.perplexity.ai/hub/blog/multimodal-embeddings-beyond-a-single-vector

pplx-decider-v1.1-27b, our updated open weights multimodal decision model, is now available. It scores the highest on the new @huggingface Decision Index 0.3 benchmark. pplx-decider-v1.1-27b costs half as much as v1, at $0.02 per million input tokens. https://huggingface.co/spaces/multimodalart/jev-decision-index

Introducing the Perplexity Decisions API. It's powered by pplx-decider-v1-27b, our multimodal decision model trained to output a probability distribution over a fixed set of answers instead of text. It costs $0.04/million input tokens and scores 85.71% across benchmarks.

Viggle Turbo v0.3 for Qwen-Image-2.1 is out! - Less grain than v0.2.1, a touch softer - New 9-step mode: finer detail, small text - ComfyUI: LoRA or single-file int8/fp8/GGUF Model: https://huggingface.co/Viggle/Qwen-Image-2.1-viggle-turbo

We built a new way to train contextual embedding models, which encode each chunk of a document with the whole document in view. pplx-embed-v2-context-9b-preview sets a new state of the art on ConTEB and @turbopuffer's new, privately held context-bench. https://www.perplexity.ai/hub/blog/contextual-embedding-beyond-the-gold-passage

Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come. But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility. This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems. Together, we are building the foundation of the AI economy. Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. https://nvda.ws/4hcoq7m

Qwen-Image-2.1 in 4 steps is here ⚡ @ViggleAI distilled Qwen-Image-2.1 into a 4-step turbo model, 6× faster, and holds up side by side with the full model ▶️ on Spaces https://hf.co/spaces/Viggle/Qwen-Image-2.1-viggle-turbo